Weak supervision uses noisy, indirect, or programmatically generated labels to create training or evaluation signals at scale. Sources can include heuristics, rules, existing classifiers, metadata, user behavior, or LLM-generated labels.
Weak supervision is useful when human labels are expensive, but it should be treated as imperfect. Developers should validate weak labels against a smaller high-quality human-labeled set before using them for decisions that matter.